Springer Meta-Analysis: Methods for Health and Experimental Studies (Statistics for Biology and Health)

This book focuses on performing hands-on meta-analysis using MetaXL, a free add-on to MS Excel. The illustrative examples are taken mainly from medical and health sciences studies, but the generic methods can be used to perform meta-analysis on data from any other discipline. The book adopts a step-by-step approach to perform meta-analyses and interpret the results. Stata codes for meta-analyses are also provided. All popularly used meta-analytic methods and models – such as the fixed effect model, random effects model, inverse variance heterogeneity model, and quality effect model – are used to find the confidence interval for the effect size measure of independent primary studies and the pooled study. In addition to the commonly used meta-analytic methods for various effect size measures, the book includes special topics such as meta-regression, dose-response meta-analysis, and publication bias. The main attraction for readers is the book’s simplicity and straightforwardness in conducting actual meta-analysis using MetaXL. Researchers would easily find everything on meta-analysis of any particular effect size in one specific chapter once they could determine the underlying effect measure. Readers will be able to see the results under different models and also will be able to select the correct model to obtain accurate results. From the Back Cover This book focuses on performing hands-on meta-analysis using MetaXL, a free add-on to MS Excel. The illustrative examples are taken mainly from medical and health sciences studies, but the generic methods can be used to perform meta-analysis on data from any other discipline. The book adopts a step-by-step approach to perform meta-analyses and interpret the results. Stata codes for meta-analyses are also provided. All popularly used meta-analytic methods and models – such as the fixed effect model, random effects model, inverse variance heterogeneity model, and quality effect model – are used to find the confidence interval for the effect size measure of independent primary studies and the pooled study. In addition to the commonly used meta-analytic methods for various effect size measures, the book includes special topics such as meta-regression, dose-response meta-analysis, and publication bias. The main attraction for readers is the book’s simplicity and straightforwardness in conducting actual meta-analysis using MetaXL. Researchers would easily find everything on meta-analysis of any particular effect size in one specific chapter once they could determine the underlying effect measure. Readers will be able to see the results under different models and also will be able to select the correct model to obtain accurate results. About the Author Shahjahan Khan is a Professor of Statistics in School of Sciences, Faculty of Health, Engineering and Sciences at the University of Southern Queensland, Toowoomba, Australia. He received his BSc (Hon) and MSc Degreesin Statistics from Jahangirnagar University, Bangladesh, and MSc and PhD Degrees in Mathematical Statistics from the University of Western Ontario, Canada. His research interest includes evidence-based decision, development of meta-analytical methods and applications in health sciences, predictive inference, shrinkage and preliminary test estimation and test for linear models with non-sample prior information. He is an elected Expatriate Fellow of Bangladesh Academy of Sciences and a recipient of prestigious Q M Hossain Gold Medal of Bangladesh Statistical Association, and ISESCO and ISOSS Gold Medals. He started his teaching career at the University of Dhaka, Bangladesh and worked in ten different countries in Australia, Europe, North America, Middle East and South East Asia.